# Tips on JIT Programming¶

BrainPy heavily relies on JIT compilation. Minimal knowledge about JIT programming will make you code much faster codes on BrainPy.

## Global variable cannot be updated¶

JIT compilers treat global variables as compile-time constants, which means during the updating of step functions, global variables will not be updated. Anything you want to modify must be passed as an argument in the step functions. For example, if you define a 1D array, and at every call of the step function, some value want to be stored in the array:

import brainpy as bp
import numpy as np

array_to_store = np.zeros(100)

def update(_i_):
array_to_store[_i_] = np.random.random()

neu = bp.NeuType('test', steps=update, ...)


The update function in defined neu will not work actually. array_to_store will not be modified. Instead, you can update array_to_store by passing it into the function as the argument:

def update(_i_, array_to_store):
array_to_store[_i_] = np.random.random()

neu = bp.NeuType('test', steps=update, ...)


This will work.

## Avoid containers¶

JIT compilers (like Numba, JAX) support containers, such as dict, namedtuple. However, the computation based in containers will greatly reduces the performance.

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